Exploring the use of the verbal intelligence quotient as a proxy for language ability in autism spectrum disorder
Bibliographic record
Abstract
There is growing interest in understanding the brain and language associations in Autism Spectrum Disorder (ASD). A considerable number of studies investigating these associations have used the verbal intelligence quotient (VIQ) as their primary measure of language form and content. Given this current trend, we aimed to establish whether the VIQ could reliably be used as a measure of receptive and expressive language form and content in individuals with ASD and in typical development (TD). We examined the VIQ standard scores derived from a Wechsler cognitive battery as well as receptive and expressive language standard scores from the Oral Written Language Scales – Second Edition (OWLS-II) of 714 participants aged 3–21 years: 488 with ASD and 226 with TD. Regression analyses revealed that VIQ scores predicted greater variance in receptive and expressive language scores in males with ASD relative to males with TD, and predicted less variance in receptive and expressive language scores in females with ASD relative to females with TD. Overall, VIQ accounted for a small proportion of variance in receptive and expressive language scores. Our findings indicate that the VIQ does not accurately capture language form and content evaluated by language measures like the OWLS-II, but may perhaps be used as a proxy for language content only.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".